Fingerprint images are widely used for personal identification purposes, in particular in pass entry systems, automated dactyloscopic identification systems, and similar dactyloscopic systems. Automatic recognition of a fingerprint pattern consists in forming an image skeleton of the original pattern and determining dactyloscopic features, namely endings and triplets. Prior to skeletonizing, where possible, noises are removed from the original image by digital filtering. The main content of a fingerprint image is a dermal lines pattern. Typically, the whole fingerprint image has varying quality. Dermal lines of some regions are unidentifiable due to the lack of characteristic ridges and concaves because of skin defects or partial loss of the pattern information during the fingerprint formation, capture and processing. Factors affecting the quality of a fingerprint image are set forth below:                original condition of a skin fingerprint pattern;                    aggressive influences, mechanical influences, wrinkles, age-related changes, and changes caused by skin diseases;            optical system quality, digitization quality, focusing errors, optical distortions, low resolution, insufficient or excessive contrast, nonlinear luminance transmission;            noises peculiar to a dynamic process of image formation, such as deformation, motion aberration, image break, dirt, etc.                        
These noises in the aggregate result in that some regions of a fingerprint image become unidentifiable. When forming an image skeleton for such a region, the system “detects” and marks false dactyloscopic features on the image. Such false features hinder automatic comparison of given print to other prints, because the probability of type 1 and 2 errors is increased. Thus, automatic processing of unidentifiable areas when recognizing fingerprint patterns results in dramatic deterioration of the dactyloscopic system characteristics. The problem can be solved by detecting such regions of unidentifiable pattern and excluding them from the pattern to be skeletonized, and further determining dactyloscopic features. In first dactyloscopic systems, an expert determined visually the quality of image regions, and marked unidentifiable regions on the image. Said method was sufficiently precise, but very laborious. Therefore, different automatic methods for evaluating a fingerprint pattern are presently used. A measure of image region quality obtained someway or other is used to decide whether further processing of said region is reasonable. Furthermore, in some dactyloscopic systems, the measure of image region quality is used as a weight factor of a dactyloscopic feature found on said region when automatically comparing two patterns.
U.S. Pat. No. 5,963,656 discloses a method for determining the quality of fingerprint images. Said method includes selecting at least one block of pixels in a fingerprint image and determining whether the selected block of pixels has a prominent direction with further referring them to directional or non-directional blocks, respectively. Then, given block is determined as a foreground block or background block, depending on the intensity of pixels of the block compared with neighboring pixels. For this purpose, the sum of intensity differences between each pixel in the block and neighboring pixels is compared with a background threshold, said each pixel being classified as a foreground pixel if said sum is higher than said background threshold; otherwise, said each pixel is classified as a background pixel. Then the amount of background pixels in each block is compared to the block threshold. If said block threshold is exceeded, the whole block is determined as background; otherwise, the block is determined as foreground. Further, the regions are formed containing adjacent directional foreground blocks, the regions being used during further processing of the fingerprint image. According to said method, the image quality measure is determined as a ratio of the areas of all regions formed in this way to the area of the whole fingerprint image.
The US patent application 20060120575 discloses a method for classifying fingerprint image quality, the method including steps of dividing a fingerprint image into a plurality of blocks; calculating and vectorizing parameters to determine the quality of each block; obtaining quality classification for each block based on said parameters; and selecting a representative value of the quality classification values, which is defined as a measure of quality of the dactyloscopic image.
The measure of quality evaluated according to the above-mentioned methods does not consider characteristics of the digital filtering system, and therefore is not optimal for determining regions of identifiable and unidentifiable pattern when digitally filtering using different digital filtering and processing systems.
Bergengruen (see. O. Bergengruen. Matching of fingerprint images. Technical report. Dept. of Numerical Analysis (CeCal), School of Engineering, University of the Republic of Uruguay, Montevideo. 1994.) proposed a quality measure based on the evaluation of the signal/noise ratio in each point. Such evaluation presumes that the image improved by filtering can be classified as “true image”, and the luminance range difference between the original image and the improved image is classified as “noise”. Bergengruen also suggested using the quality measure calculated in this way as weight factors in comparison algorithms. This method allows of determining regions of a fingerprint image where pattern is destroyed irretrievably. However, the regions having low signal/noise ratio but with recoverable pattern will also be classified as unidentifiable. In particular, this occurs on the regions of an image where dermal lines are strongly fragmented due to peculiarities of a person's skin structure. According to said method, such regions will be classified as unidentifiable.
Thus, strongly fragmented dermal lines, for example dermal lines consisting of dots or short sections, cannot be classified as recoverable according to known methods, though they can be visually perceived by an examiner.